Image registration geometrically aligns two images with different viewing geometry and/or different terrain distortions into the same coordinate system so that the corresponding pixels represent the same objects. Geometric relationship between a sensed image and a reference image can often be obtained through a number of tie points, and the relationship between images can be obtained using transformation capabilities. Automatic tie point generation and accurate image-to-image registration improves usability in many applications including georeferencing, change detection and time series analysis, data fusion, the formation of image mosaics, digital elevation model (DEM) extraction, 3D modeling, video compression and motion analysis, etc.
Known image registration methods involve four basic steps: 1) feature selection, 2) feature matching based on a similarity measure, 3) transformation model determination, and 4) image transformation and resampling. Image registration methods provide different combinations of choices for the four components. Comprehensive reviews of image registration methods include: L. Brown, “A survey of image registration techniques,” ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, 1992; and B. Zitova and J. Flusser, “Image registration methods: a survey,” Image and Vision Computing, vol. 21, pp. 977-1000, 2003. The contents of both of those references are incorporated herein by reference. Reviews on remote sensing applications can be found in: J. Inglada and A. Giros, “On the possibility of automatic multisensor image registration,” IEEE Trans. Geosci. Remote Sensing, vol. 42, no. 10, pp. 2104-2120, October 2004. The contents of that reference are incorporated herein by reference.
In manual registration, a human operator utilizes interactive software to visually perform the tasks of locating and matching feature points (called “tie” points) between two images. Tie points act as pairs of points—one from each of the images used in the registration process. The process is repetitive, laborious, tedious and prone to error. Manual registration becomes prohibitive for a large amount of data and a large area.
Remote sensing images, however, have various characteristics that make it difficult to automatically generate tie points and align images. For example, location errors may occur in the navigation and during spacecraft maneuvers. Similarly, atmospheric scattering and absorption affect the fidelity of remote sensing data. Image artifacts may occur due to lens flare, noise or defective pixels. Furthermore, various image characteristics such as multi-temporal effects, terrain effects, different sensor modalities and different spatial resolution all add complexity to automated registration.